Analytics and Modeling

This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns.

Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized. 

 

Methodological Context Surface & Field Analyses Space-Time Analysis & Modeling
Geospatial Analysis & Model Building Modeling Surfaces Time Geography
Changing Context of GIScience Gridding, Interpolation, and Contouring Capturing Spatio-Temporal Dynamics in Computational Modeling 
Building Blocks Inverse Distance Weighting GIS-Based Computational Modeling
Overlay & Combination Operations Radial Basis & Spline Functions Computational Movement Analysis
Areal Interpolation Polynomial Functions Volumes and Space-Time Volumes
Aggregation of Spatial Entities Kriging Interpolation  
Classification & Clustering LiDAR Point Cloud Analysis Geocomputational Methods & Models
Boundaries & Zone Membership Intervisibility, Line-of-Sight, and Viewsheds Cellular Automata
Spatial Queries Digital Elevation Models & Terrain Metrics Agent-based Modeling
Buffers TIN-based Models and Terrain Metrics Simulation Modeling
Grid Operations & Map Algebra Watersheds & Drainage Artificial Neural Networks
Data Exploration & Spatial Statistics 3D Parametric Surfaces Genetic Algorithms & Evolutionary Computing 
Spatial Statistics Network & Location Analysis Big Data & Geospatial Analysis
Spatial Sampling for Spatial Analysis Intro to Network & Location Analysis Problems & with Large Spatial Databases
Exploratory Spatial Data Analysis (ESDA) Location & Service Area Problems Pattern Recognition & Matching
Point Pattern Analysis Network Route & Tour Problems Artificial Intelligence Approaches
Kernels & Density Estimation Modelling Accessibility Intro to Spatial Data Mining
Spatial Interaction Location-allocation Modeling Rule Learning for Spatial Data Mining
Cartographic Modeling The Classic Transportation Problem Machine Learning Approaches
Multi-criteria Evaluation   CyberGIS and Cyberinfrastructure
Grid-based Statistics and Metrics   Analysis of Errors & Uncertainty
Landscape Metrics   Error-based Uncertainty
Hot-spot and Cluster Analysis   Conceptual Models of Error & Uncertainty
Global Measures of Spatial Association   Spatial Data Uncertainty
Local Indicators of Spatial Autocorrelation   Problems of Scale & Zoning
Simple Regression & Trend Surface Analysis   Thematic Accuracy & Assessment
Geographically Weighted Regression   Stochastic Simulation & Monte Carlo Methods
Spatial Autoregressive & Bayesian Methods   Mathematical Models of Uncertainty
Spatial Filtering Models   Fuzzy Aggregation Operators

 

AM-107 - Spatial Data Uncertainty

Although spatial data users may not be aware of the inherent uncertainty in all the datasets they use, it is critical to evaluate data quality in order to understand the validity and limitations of any conclusions based on spatial data. Spatial data uncertainty is inevitable as all representations of the real world are imperfect. This topic presents the importance of understanding spatial data uncertainty and discusses major methods and models to communicate, represent, and quantify positional and attribute uncertainty in spatial data, including both analytical and simulation approaches. Geo-semantic uncertainty that involves vague geographic concepts and classes is also addressed from the perspectives of fuzzy-set approaches and cognitive experiments. Potential methods that can be implemented to assess the quality of large volumes of crowd-sourced geographic data are also discussed. Finally, this topic ends with future directions to further research on spatial data quality and uncertainty.

AM-33 - Spatial filtering
  • Identify modeling situations where spatial filtering might not be appropriate
  • Demonstrate how spatial autocorrelation can be “removed” by resampling
  • Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem
  • Explain how the Getis and Tiefelsdorf-Griffith spatial filtering techniques incorporate spatial component variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals
  • Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset
  • Describe the relationship between factorial kriging and spatial filtering
AM-10 - Spatial Interaction

Spatial interaction (SI) is a fundamental concept in the GIScience literature, and may be defined in numerous ways. SI often describes the "flow" of individuals, commodities, capital, and information over (geographic) space resulting from a decision process. Alternatively, SI is sometimes used to refer to the influence of spatial proximity of places on the intensity of relations between those places. SI modeling as a separate research endeavor developed out of a need to mathematically model and understand the underlying determinants of these flows/influences. Proponents of SI modeling include economic geographers, regional scientists, and regional planners, as well as climate scientists, physicists, animal ecologists, and even some biophysical/environmental researchers. Originally developed from theories of interacting particles and gravitational forces in physics, SI modeling has developed through a series of refinements in terms of functional form, conceptual representations of distances, as well as a range of analytically rigorous technical improvements.
 

AM-14 - Spatial process models
  • Discuss the relationship between spatial processes and spatial patterns
  • Differentiate between deterministic and stochastic spatial process models
  • Describe a simple process model that would generate a given set of spatial patterns
AM-26 - Spatial sampling for statistical analysis
  • List and describe several spatial sampling schemes and evaluate each one for specific applications
  • Differentiate between model-based and design-based sampling schemes
  • Design a sampling scheme that will help detect when space-time clusters of events occur
  • Create spatial samples under a variety of requirements, such as coverage, randomness, and transects
  • Describe sampling schemes for accurately estimating the mean of a spatial data set
AM-42 - The Classic Transportation Problem

The classic transportation problem concerns minimizing the cost of transporting a single product from sources to destinations. It is a network-flow problem that arises in industrial logistics and is considered as a special case of linear programming. The total number of units produced at each source, the total number of units required at each destination and the cost to transport one unit from each source to each destination are the basic inputs. The objective is to minimize the total cost of transporting the units produced at sources to meet the demands at destinations. The problem solution includes three basic steps: 1) finding an initial basic feasible solution, 2) checking if the current solution is optimal (with the lowest costs), and 3) improving the current solution through iteration. Modeling and solving the classic transportation problem rely strongly on network models, least-cost path algorithms, and location-allocation analysis in the field of geographic information science (GIScience). Thus, it represents a key component in the network analytics and modeling area of GIS&T.

AM-34 - The Geographically Weighted Regression Framework

Local multivariate statistical models are increasingly encountered in geographical research to estimate spatially varying relationships between a response variable and its associated predictor variables. In geography and many other disciplines, such models have been largely embedded within the framework of regression and can reveal significantly more information about the determinants of observed spatial distribution of the dependent variable than their global regression model counterparts. This section introduces one type of local statistical modeling framework: Geographically Weighted Regression (GWR). Models within this framework estimate location-specific parameter estimates for each covariate, local diagnostic statistics, and bandwidth parameters as indicators of the spatial scales at which the modeled processes operate. These models provide an effective means to estimate how the same factors may evoke different responses across locations and by so doing, bring to the fore the role of geographical context on human preferences and behavior.

AM-86 - Theory of error propagation
  • Describe stochastic error models
  • Exemplify stochastic error models used in GIScience
AM-59 - Vector-to-raster and raster-to-vector conversions
  • Explain how the vector/raster/vector conversion process of graphic images and algorithms takes place and how the results are achieved
  • Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation
  • Illustrate the impact of vector/raster/vector conversions on the quality of a dataset
  • Convert vector data to raster format and back using GIS software
AM-89 - Weighting schemes
  • Evaluate a fuzzy weighting scheme in terms of uncertainty and error propagation

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